The work performed may be divided into two sections, development of spatial microsimulation (SM) framework for improved climate impact analysis and the micro-level distributional evaluation of climate impacts. This resulted in three working papers. It should be noted that, with minor amendments, the three work packages of this action have been delivered despite the action ending 4 months early. Some minor amendments to WP3 were applied to allow for this time change.
The first goal of this project was to create a new method known as ‘Conditional Monte Carlo’ (CMC). This is a spatial microsimulation methodology to quantify spatially-explicit micro-level welfare analysis of climate impacts. This method is that it overcomes many data limitations presented by existing methods; it may be used for contexts where previous methods provided unreliable results. Further, the data burden is less onerous than other methods. This flexibility facilitated a robust validation procedure where Mexican socioeconomic data were used. Mexico is the only country for which data is collected at the small area level. This provides a most robust and credible benchmark of model performance, against which the procedure performed exceptionally well, simulating welfare with a degree of precision unprecedented in the literature. The attached figure provides insight into how well the procedure has reproduced the socioeconomic data. A working paper was produced. It is envisaged that this paper will be submitted to a 3* or 4-ranked economics journal on the ABS scale.
The second objective demonstrated the use of the SM model for climate impact analysis and adaptation decision-making. PIK climate scientists aided the PI, Dr. Farrell, with climate impact modelling and Dr. Farrell then proceeded to link this climate impact analysis with the socioeconomic data for the CMC procedure.
A working paper links socioeconomic data with climate impact data on floods and temperature extremes. It shows that current methods have aggregation bias and also shows the socioeconomic distributional impacts of climate change in Mexico, something previously untested in the literature. It is envisaged that this paper will be submitted to Nature Climate Change or a similar high-impact journal.
The third working paper assessed the impacts new technology adoption may have for food security and climate change adaptation. This was carried out in conjunction with climate scientists at PIK who aided in the linking of climate data with socioeconomic microdata. This paper shows that improved maize seed increases food availability in developing countries, potentially mitigating the negative effects of climate change, but the distribution of this benefit may not necessarily remedy food insecurity. Adoption is often biased towards well-resourced households. The corrections required for overcoming this bias can be identified through a novel analysis of the distribution of adoption determinants across the income spectrum, where targeting improved seed adoption towards households headed by older persons, uneducated, those with off-farm income and households headed by females yields equal adoption across the welfare spectrum.
This paper highlights the importance of looking beyond aggregate effects and to understand the distribution of impacts to fully understand the effectiveness of a climate adaptation intervention. This paper is currently in working paper format and will be submitted to The ‘American Journal of Agricultural Economics’ or a similarly high-impact journal.
All working papers have yet to pass peer review. Dissemination will occur after peer review, in line with common practice. Further public dissemination will be facilitated through podcast publication, a research bulletin and website dissemination, upon passing peer review. A podcast has been established through this action and this will feature dissemination of this work upon peer-review. This work has been submitted for presentation at academic conferences in 2020. International regional science and climate economics conferences have been targetted.
